Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning
This work addresses turbulence modeling for fluid dynamics applications, but it appears incremental as it evaluates existing methods on new data.
The paper tackled predicting turbulent flows at high Reynolds numbers by using deep learning, specifically Wasserstein GANs trained on high-resolution DNS data, and showed qualitatively good agreement and quantitative statistical assessment of generated turbulent structures.
In this paper, deep learning (DL) methods are evaluated in the context of turbulent flows. Various generative adversarial networks (GANs) are discussed with respect to their suitability for understanding and modeling turbulence. Wasserstein GANs (WGANs) are then chosen to generate small-scale turbulence. Highly resolved direct numerical simulation (DNS) turbulent data is used for training the WGANs and the effect of network parameters, such as learning rate and loss function, is studied. Qualitatively good agreement between DNS input data and generated turbulent structures is shown. A quantitative statistical assessment of the predicted turbulent fields is performed.